NDLI: Joint learning of convolution neural networks for RGB-D-based human action recognition

Content Provider	IET Digital Library
Author	Ren, Ziliang Zhang, Qieshi Qiao, Piye Niu, Maolong Gao, Xiangyang Cheng, Jun
Abstract	RGB-D-based human action recognition aims to learn distinctive features from different modalities and has shown good progress in practice. However, it is difficult to improve the recognition performance through directly training multiple individual convolutional networks (ConvNets) and fusing features later because complmentary information between different modalities cannot be learned. To address this issue, this Letter proposes a single two-stream ConvNets framework for multimodality learning that extract features through RGB and depth streams. Specifically, the authors first represent RGB-D sequence to motion images as the inputs of the proposed ConvNets for obtaining spatial–temporal information. Then, a features fusion and joint training strategy is adapted to learn RGB-D complementary features simultaneously. Experimental results on benchmark NTU RGB+D 120 dataset validate the effectiveness of the proposed framework and demonstrate that two-stream ConvNets outperforms the current state-of-the-art approaches.
Starting Page	1112
Ending Page	1115
Page Count	4
ISSN	00135194
Volume Number	56
e-ISSN	1350911X
Issue Number	Issue 21, Oct (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/el/56/21
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/el.2020.2148
Journal	Electronics Letters
Publisher Date	2020-09-01
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Benchmark NTU RGB+D 120 Dataset Complmentary Information Computer Vision And Image Processing Technique Convolution Neural Network Convolutional Neural Nets Depth Streams Feature Extraction Feature Fusion Image Colour Analysis Image Recognition Image Representation Joint Learning Joint Training Strategy Knowledge Engineering Technique Learning in AI Multimodality Learning Multiple Individual Convolutional Network Neural Computing Technique Object Recognition Recognition Performance RGB-D Complementary Feature RGB-D Sequence RGB-D-based Human Action Recognition Aims Spatial–temporal Information Two-stream ConvNets Framework
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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